Jiaojiao Guan

VirProtRAG improves viral protein function annotation by integrating literature retrieval and evidence-based generation, introducing 32.53% new annotations and supporting sequence-inferred functions.

PlasRAG characterizes and retrieves plasmids using sequence-text alignment, showing that plasmids with broader host ranges and ARGs spread more extensively.

MOSTPLAS improves plasmid host range prediction by addressing missing labels in training data through self-correction and pseudo label learning.

ViraLM is a viral language model that improves virus detection in metagenomic data, particularly for short contigs, by leveraging a genome foundation model and a curated dataset.

PlasGO predicts GO terms for plasmid-encoded proteins using a hierarchical model, achieving high precision in function annotation.